Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor
To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/16/6316 |
| _version_ | 1797431677212950528 |
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| author | Hongtao Xue Ziwei Song Meng Wu Ning Sun Huaqing Wang |
| author_facet | Hongtao Xue Ziwei Song Meng Wu Ning Sun Huaqing Wang |
| author_sort | Hongtao Xue |
| collection | DOAJ |
| description | To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a K-means clustering and AdaBoost algorithm to solve the lower accuracy and poorer stability of traditional AHNs. Firstly, K-means clustering is used to improve the interval updating method of any adjacent AHNs molecules, and then simplify the complexity of the AHNs model. Secondly, the AdaBoost algorithm is utilized to adaptively distribute the weights for multiple weak models, then reconstitute the network structure of the AHNs. Finally, double-optimized AHNs are used to build an intelligent diagnosis system, where two cases of bearing datasets from Paderborn University and a self-made IWM test stand are processed to validate the better performance of the proposed method, especially in multiple rotating speeds and the load conditions of the IWM. The double-optimized AHNs provide a higher accuracy for identifying the mechanical faults of the IWM than the traditional AHNs, K-means-based AHNs (K-AHNs), support vector machine (SVM), and particle swarm optimization-based SVM (PSO-SVM). |
| first_indexed | 2024-03-09T09:48:40Z |
| format | Article |
| id | doaj.art-28705c84089940d28ed3fbf400997f91 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:48:40Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-28705c84089940d28ed3fbf400997f912023-12-02T00:17:58ZengMDPI AGSensors1424-82202022-08-012216631610.3390/s22166316Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel MotorHongtao Xue0Ziwei Song1Meng Wu2Ning Sun3Huaqing Wang4School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaBosch Automotive Products (Suzhou) Co., Ltd., Suzhou 215021, ChinaCollege of Automotive and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, ChinaTo avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a K-means clustering and AdaBoost algorithm to solve the lower accuracy and poorer stability of traditional AHNs. Firstly, K-means clustering is used to improve the interval updating method of any adjacent AHNs molecules, and then simplify the complexity of the AHNs model. Secondly, the AdaBoost algorithm is utilized to adaptively distribute the weights for multiple weak models, then reconstitute the network structure of the AHNs. Finally, double-optimized AHNs are used to build an intelligent diagnosis system, where two cases of bearing datasets from Paderborn University and a self-made IWM test stand are processed to validate the better performance of the proposed method, especially in multiple rotating speeds and the load conditions of the IWM. The double-optimized AHNs provide a higher accuracy for identifying the mechanical faults of the IWM than the traditional AHNs, K-means-based AHNs (K-AHNs), support vector machine (SVM), and particle swarm optimization-based SVM (PSO-SVM).https://www.mdpi.com/1424-8220/22/16/6316intelligent diagnosisin-wheel motorartificial hydrocarbon networksK-means clusteringAdaBoost algorithm |
| spellingShingle | Hongtao Xue Ziwei Song Meng Wu Ning Sun Huaqing Wang Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor Sensors intelligent diagnosis in-wheel motor artificial hydrocarbon networks K-means clustering AdaBoost algorithm |
| title | Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor |
| title_full | Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor |
| title_fullStr | Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor |
| title_full_unstemmed | Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor |
| title_short | Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor |
| title_sort | intelligent diagnosis based on double optimized artificial hydrocarbon networks for mechanical faults of in wheel motor |
| topic | intelligent diagnosis in-wheel motor artificial hydrocarbon networks K-means clustering AdaBoost algorithm |
| url | https://www.mdpi.com/1424-8220/22/16/6316 |
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